Wildlife Density Data Better Predicts Conservation Success

More detailed data translates into better conservation plans

June 10, 2015

PETALUMA, California—A recent study published in the journal Conservation Biology makes a strong case for a new approach to conservation planning that uses much more robust data sets in order to better protect birds, plants, and animals.

The concept is fairly simple, but won’t work unless scientists can agree to share data across studies.

“Right now, we primarily only use presence and absence data for species when conservation planning for large landscapes. Much of this is due to the cost and time of collecting more comprehensive data,” said the study’s lead author, Sam Veloz, climate adaptation group leader at Point Blue Conservation Science. “We can do a much better job of conservation planning if we include data on individual species richness, not just whether they are present.”

To illustrate his point, Veloz and his research team encouraged partners to make their detailed bird observation data accessible through the Avian Knowledge Network leading to the addition of over 900,000 new bird observations from 23 different studies. They then combined the information with bird data in the California Avian Data Center. The team used the data to develop species distribution and density models covering coastal Northern California, Oregon and Washington for 26 species of land birds representing four different habitat types.

They then mapped conservation priorities based on both the presence/absence and density models, and compared the estimated population size of each species protected against the conservation priorities developed using each approach.

“As expected, we found that the prioritizations based on count data protected more individuals of each species than the prioritizations based on presence/absence data in the areas of highest conservation priority,” Veloz said. “We found that conservation priorities developed using presence/absence data over-valued areas of low conservation importance and undervalued areas of high conservation importance relative to priorities developed using density models.”

Veloz sees the main challenge is getting scientists from across the conservation spectrum to share their high-quality count data of individual species, not matter the study size, so planners can have as broad a data set as possible when drawing up conservation plans.

“This study shows the value of researchers sharing their data. We can combine and recycle data from multiple studies, and re-use it to answer larger conservation questions,” Veloz said. “If we all worked together to share data, we could better prioritize and protect important habitat.”
To read more about the study, visit http://onlinelibrary.wiley.com/doi/10.1111/cobi.12499/abstract.